R/pot_dir.R

#' Compute NOS using a directed network and with a user provided network#' of potential interactions#'#' @description Computation of NOS using an directed network (e.g. a#' food web) and with a user provided network of potential interactions.#' @usage NOSM_POT_dir(net, pot_net, perc = 1, sl = 1)#' @param net A network, in the form of an edge list. This should be a matrix or#' dataframe with two columns. Each value in a column is a node. Nodes can be#' identified using numbers or characters.Data can also be in the format of a#' frequency interaction matrix, as used in the \link[bipartite]{bipartite} R#' package. In these cases \code{\link{freqMat_2_edge}} should be used first,#' to convert the interaction matrix to an edge list.#' @param pot_net A network of all potential interactions. These should include,#' as a minimum, all the observed interactions (i.e. all links in net),plus#' any other possible interaction (such as all those permitted by a certain#' trophic rule). pot_net should have the same structure as net (e.g. it#' should be a data frame or matrix).#' @param perc (default to 1) - the fraction of node pair comparisons to be#' performed to compute NOS. We recommend performing all possible pair#' comparisons (perc = 1). However, for exploratory analyses on large sets of#' networks (or for very large networks), the possibility of using a lower#' fraction of pair comparisons is a useful option.#' @param sl (default is 1) Specifies whether cannibalistic interactions should#' be considered as possible and therefore taken into account and removed#' during computation ('1') or not ('0').#' @return A list (two elements) of class 'NOSM' with a 'Type' attribute#' 'Pot_dir'. The first element in the list is a vector of overlap values for#' the "in nodes" and the second element is a vector of overlap values for the#' "out nodes".#'#' The \code{\link{summary.NOSM}} methods provides more useful summary#' statistics.#' @examples#' data(boreal)#' y <- boreal[1:300,] #subset 300 rows for speed#' d <- sample(nrow(y), 200, replace = FALSE) #create a random pot_net#' pot_net <- y[d,] #by randomly sampling 200 rows from boreal#' x <- NOSM_POT_dir(y, pot_net, perc = 1, sl = 1)#' summary(x)#' @exportNOSM_POT_dir<-function(net,pot_net,perc =1, sl =1){
x <-adj(net, pot_net)
OV_in <-OV(x$adj_in, x$pot_in, perc, sl)
OV_out <-OV(x$adj_out, x$pot_out, perc, sl)
y <-list("ov_in"= OV_in,"ov_out"= OV_out)class(y)<-"NOSM"attr(y,"Type")<-"Pot_dir"return(y)}